Data-Driven Dynamical Systems with Machine Learning

Data-Driven Dynamical Systems with Machine Learning

Explore a comprehensive 15-hour lecture series on data-driven dynamical systems and machine learning. Delve into modern methods for modeling complex systems from data, with a focus on data-driven control. Cover topics such as Dynamic Mode Decomposition, Sparse Identification of Nonlinear Dynamics (SINDy), Koopman Spectral Analysis, and Reinforcement Learning. Learn to simulate systems like the Lorenz System and Logistic Map in MATLAB, and discover how to apply machine learning techniques to fluid mechanics and aeroelastic models. Gain insights into deep learning applications for physics discovery and reduced-order modeling. Understand the integration of optimization algorithms, autoencoders, and physics-informed machine learning in building robust dynamical systems models. Ideal for those interested in the intersection of data science, engineering, and machine learning applied to complex dynamical systems.